Neuroimage. 2018 Oct 15;180(Pt B):632-645. doi: 10.1016/j.neuroimage.2017.10.022. Epub 2017 Oct 14.
Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis.
Du Y1, Fryer SL2, Fu Z3, Lin D3, Sui J4, Chen J3, Damaraju E3, Mennigen E5, Stuart B6, Loewy RL6, Mathalon DH2, Calhoun VD5.
Author information
1
The Mind Research Network, Albuquerque, NM, USA; School of Computer & Information Technology, Shanxi University, Taiyuan, China. Electronic address: [email protected].
2
Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA; The Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, USA.
3
The Mind Research Network, Albuquerque, NM, USA.
4
The Mind Research Network, Albuquerque, NM, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
5
The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA.
6
Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA.
Abstract
Individuals at clinical high-risk (CHR) for psychosis are characterized by attenuated psychotic symptoms. Only a minority of CHR individuals convert to full-blown psychosis. Therefore, there is a strong interest in identifying neurobiological abnormalities underlying the psychosis risk syndrome. Dynamic functional connectivity (DFC) captures time-varying connectivity over short time scales, and has the potential to reveal complex brain functional organization. Based on resting-state functional magnetic resonance imaging (fMRI) data from 70 healthy controls (HCs), 53 CHR individuals, and 58 early illness schizophrenia (ESZ) patients, we applied a novel group information guided ICA (GIG-ICA) to estimate inherent connectivity states from DFC, and then investigated group differences. We found that ESZ patients showed more aberrant connectivities and greater alterations than CHR individuals. Results also suggested that disease-related connectivity states occurred in CHR and ESZ groups. Regarding the dominant state with the highest contribution to dynamic connectivity, ESZ patients exhibited greater impairments than CHR individuals primarily in the cerebellum, frontal cortex, thalamus and temporal cortex, while CHR and ESZ populations shared common aberrances mainly in the supplementary motor area, parahippocampal gyrus and postcentral cortex. CHR-specific changes were also found in the connections between the superior frontal gyrus and calcarine cortex in the dominant state. Our findings suggest that CHR individuals generally show an intermediate functionalconnectivity pattern between HCs and SZ patients but also have unique connectivity alterations.
Copyright © 2017 Elsevier Inc. All rights reserved.
KEYWORDS:
Clinical high-risk; Connectivity state; Dynamic functional connectivity; ICA; Schizophrenia; fMRI
PMID:
29038030
PMCID:
PMC5899692
[Available on 2019-10-15]
DOI:
10.1016/j.neuroimage.2017.10.022
Hum Brain Mapp. 2017 May;38(5):2683-2708. doi: 10.1002/hbm.23553. Epub 2017 Mar 10.
Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.
Du Y1,2, Pearlson GD3,4,5, Lin D1, Sui J1,6, Chen J1, Salman M1,7, Tamminga CA8, Ivleva EI8, Sweeney JA8,9, Keshavan MS10, Clementz BA11, Bustillo J12, Calhoun VD1,3,7,12.
Author information
Abstract
Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017.
© 2017 Wiley Periodicals, Inc.
KEYWORDS:
bipolar disorder; dynamic functional connectivity; functional magnetic resonance imaging; independent component analysis; schizoaffective disorder; schizophrenia
PMID:
28294459
PMCID:
PMC5399898
DOI:
10.1002/hbm.23553
[Indexed for MEDLINE]
Free PMC Article
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Neuroimage. 2018 Oct 15;180(Pt B):619-631. doi: 10.1016/j.neuroimage.2017.09.035. Epub 2017 Sep 20.
Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.
Fu Z1, Tu Y2, Di X3, Du Y4, Pearlson GD5, Turner JA6, Biswal BB3, Zhang Z7, Calhoun VD8.
Author information
Abstract
The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.
Copyright © 2017 Elsevier Inc. All rights reserved.
PMID:
28939432
PMCID:
PMC5860934
[Available on 2019-10-15]
DOI:
10.1016/j.neuroimage.2017.09.035
[Indexed for MEDLINE]
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